Abnormal Monitoring Data Detection Based on Matrix Manipulation and the Cuckoo Search Algorithm

Author:

Meng Zhenzhu1,Wang Yiren2,Zheng Sen3,Wang Xiao4,Liu Dan1,Zhang Jinxin1,Shao Yiting1

Affiliation:

1. School of Water Conservancy and Environment Engineering & Nanxun Innovation Institute, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China

2. School of Environment and Civil Engineering, Dongguan University of Technology & Guangdong Provincial Key Laboratory of Intelligent Disaster Prevention and Emergency Technologies for Urban Lifeline Engineering, Dongguan 523808, China

3. Laboratory of Environmental Hydraulics, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

4. Huai’an Hydraulic Surcey and Design Research Institute Co., Ltd., Huaian 223500, China

Abstract

Structural health monitoring is an effective method to evaluate the safety status of dams. Measurement error is an important factor which affects the accuracy of monitoring data modeling. Processing the abnormal monitoring data before data analysis is a necessary step to ensure the reliability of the analysis. In this paper, we proposed a method to process the abnormal dam displacement monitoring data on the basis of matrix manipulation and Cuckoo Search algorithm. We first generate a scatter plot of the monitoring data and exported the matrix of the image. The scatter plot of monitoring data includes isolate outliers, clusters of outliers, and clusters of normal points. The gray scales of isolated outliers are reduced using Gaussian blur. Then, the isolated outliers are eliminated using Ostu binarization. We then use the Cuckoo Search algorithm to distinguish the clusters of outliers and clusters of normal points to identify the process line. To evaluate the performance of the proposed data processing method, we also fitted the data processed by the proposed method and by the commonly used 3-σ method using a regression model, respectively. Results indicate that the proposed method has a better performance in abnormal detection compared with the 3-σ method.

Funder

University-Level Key Course of Zhejiang University of Water Resources and Electric Power

Nanxun Scholars Program for Young Scholars of ZJWEU

Zhejiang Provincial Natural Science Foundation of China

Publisher

MDPI AG

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